Egyptian Journal of Remote Sensing and Space Sciences最新文献

The increase in water demand and the scarcity of fresh water in arid regions have contributed to the depletion of groundwater. Artificial Groundwater Recharge (AGR) is an advanced strategy that contributes to combating water shortage issues. Limited efforts have been exerted to evaluate and demarcate AGR potential zones in the United Arab Emirates (UAE). The current study aims to delineate AGR potential zone mapping using the traditional analytical hierarchy process (AHP) and a hybrid deep learning model namely, Convolutional Neural Network-Xtreme Gradient Boosting (CNN-XGB) was used for the optimal prediction-based suitability assessment. A total of nine hydrogeological factors were considered for AGR mapping. First, the influence of each parameter was determined based on expert opinion and literature reviews for the AHP approach (0.007 consistency ratio). Second, a hybrid CNN-XGB model (90.8 % accuracy) predicted the AGR and non-AGR classes as part of binary classification and generated an AGR potential zone map. Moreover, the contributing factors were analyzed deeply for the AGR site selection to understand the intercorrelation, importance, and prediction interaction. Using both approaches, a comparative assessment was conducted in the eastern, central, and western parts of Sharjah. The AGR zone based on the CNN-XGB model achieved a precision of (0.8168), recall (0.7873), and F1-score (0.8018). The critical contributing factors for AGR mapping were found to be geology (20%), geomorphology (15%), rainfall (10%), and groundwater level (10%). The AGR map is expected to help explore new sites with potentially higher favourability to retain water, deal with water scarcity, and improve water management in the UAE.

In July 2021, a heavy rainstorm was sweeping across Henan Province, causing geological disasters such as floods, mudslides, and landslides, which seriously threatened the safety of human life and property. Precipitable water vapor (PWV) is related to the occurrence and scale of rainfall. Here, based on Global Navigation Satellite System (GNSS) observations, in-situ meteorological files (GMET), ephemeris products, ERA5 data, and weather station data, the relationship between PWV and rainstorm from July 1st to 30th was studied. The results show that GMET and ERA5 in July 2021 have high consistency in some stations, with a root mean square error (RMSE) for temperature below 1.6 °C, for pressure below 0.5 hPa, and for relative humidity below 9 %. During the week before the heavy rainstorm, the temperature dropped remarkably and the temperature difference decreased, while the relative humidity increased and the relative humidity difference decreased. Compared with ERA5 PWV, the RMSE of GNSS PWV retrieved using real-time ephemeris is 3.238 mm. Different from the normal rainfall, we found that the PWV variation during the Henan rainstorm experienced a unique “accumulation” period. We also observed a clear correlation between PWV and the rainstorm, both temporally and spatially. In addition, the PWV in the severely damaged area was 20 mm higher than the average value of the past decade. Ten days after the rainstorm, the surface of this area had subsided by 1.5–3 mm. Finally, we found that the topography of Henan, the low vortex, the north-biased subtropical high, and the double typhoons all played a role in the successful transport and deposition of water vapor.

From the 6^th to 7^th of February 2023, a storm surge struck Ras El-Bar, Nile Delta coast and attacked the resort facilities, with a wave height and velocity in deep water of 7.2 m and 12.7 m/sec respectively. The wind speed was 12.84 m/s, blowing from the NW and the WSW quadrants. This was an unwitnessed event revealed from the study of similar time interval from 1998 to 2022. Synchronizing with this event on the 6^th of February 2023, was Kahramanmaraş Turkey Earthquakes. Consequently, the shoreline receded for about −30 m and with a drop in sea-level of about −40 cm. Furthermore, considerable changes in the beach morphology from a dissipative to a cuspate-related, intermediate tidal flat transverse bar with a rip profile. These are either related to the change in the morphodynamic or sedimentary budget, and resulting due to seawater scouring of bottom sediments for more than −30 cm. Two days preceding the Earthquakes an isostatic rise in sea-level (+20 cm) at the Turkish coast compared to the Mediterranean records, which is interpreted due to regional underwater seismic activities. The drop in the sea-surface height does not happen due to seawater outflow to the Atlantic Ocean. However, the sea-level regained its normal position because of the refill occurring from the Atlantic Ocean to the Mediterranean Sea. The pumice pieces, organic peat, and starfish distributed at Ras El-Bar coast, and thrown from the Northern Mediterranean indicate that the Egyptian coast was subjected to a little tsunami with average height of 14 cm. It is minimized due to enforced wave shifting from high pressure over Egypt to the low-pressure sinks.

Ghana's coastline has been facing erosion and sedimentation phenomena for several decades, resulting in a serious threat to life and property considering that major urban settlements are located on the coast. In this region, there has been a lack of emphasis on comprehensive, large-scale investigations into coastal changes: prior research has predominantly centered on site-specific assessments. These studies have revealed alarming erosion rates, with reports indicating that nearly ten meters are lost annually. The use of high-resolution remotely sensed data can be a consistent support in regions where physical or economic obstacles interfere with collecting in situ information. In particular, the use of continuous all-weather SAR data may facilitate the evaluation of erosion and sedimentation phenomena in coastal areas. In this paper, we apply SAR data over a time period between 2017 and 2021. Sentinel-1 data are pre-processed using the Google Earth Engine platform, and a dedicated algorithm is then applied to identify and quantify erosion and sedimentation processes. Optical images are used as a reference for detecting the location of two areas where consistent sedimentation and erosion phenomena occurred in the considered four years. The results demonstrate that SAR backscattering variations over time offer a reliable method for monitoring coastal changes. This approach enables the identification of the type of phenomena occurring - sedimentation or erosion -, and allows for the quantification of their intensity and dimensions over time. The method can be worldwide applied once the appropriate thresholds are evaluated and help in predictive studies and environmental planning.

The dual use of wind and solar energy holds great promise for low-cost and high-performance green infrastructure. However, for such hybrid systems to operate successfully, comprehensive and simultaneous dimensional planning is required, a goal that single-perspective assessment approaches fail to attain. This paper proposes a novel SpatioTemporal Decision-Making (STDM) model based on Geospatial Artificial Intelligence (GeoAI) for the optimal allocation of onshore wind-solar hybrid plants, with application on a national scale in Iraq. To this end, a wide range of 21 evaluative and restrictive spatial criteria were covered. The temporal synergy factor between renewable resources was considered for the first time in this type of study. Unique global weightings for decision factors were derived using Random Forest (RF) and SHapley Additive exPlanations (SHAP) algorithms supported by sample inventories of wind and solar plants worldwide. Finally, weighted linear combination (WLC) and fuzzy overlay techniques were harnessed in a GIS environment for spatiotemporal suitability mapping of energy systems. According to the RF-SHAP model, the techno-economic criteria demonstrated substantial contributions to the placement of wind and solar systems compared with the socio-environmental criteria. The spatiotemporal suitability map identified three promising opportunities for Iraq at South Dhi-Qar, East Wasit, and West Diyala, with total areas of 780, 2166, and 649 km², respectively. We anticipate that our findings will encourage government agencies, decision-makers, and stakeholders to increase funding for clean energy transition initiatives.

The manual extraction of land disturbances associated with oil exploration, which normally includes resource roads, mining facilities, and well pads, presents significant challenges in terms of cost and time. Accurate monitoring and mapping of land disturbances resulting from oil exploration plays a crucial role in conducting comprehensive environmental assessments and facilitating effective land reclamation initiatives. However, prevailing deep learning methodologies in the realm of oil and gas exploration primarily focus on oil spill detection, neglecting the critical aspect of land disturbances resulting from oil exploration, thus overlooking the impact on land. Furthermore, given that the well sites are scattered and relatively diminutive compared to other land covers, their detection poses substantial difficulties. This paper proposes an automatic error-correcting (AEC) algorithm to address deficiencies in ground truth data quality. This AEC method was integrated into the deep-learning framework for land disturbance extraction, specifically tailored for land disturbances analysis associated with oil exploration. The efficacy of our method was validated on a dataset collected in Alberta covering an area of oil sand mining sites. The application of the AEC algorithm significantly enhanced the accuracy of land disturbance analysis, thereby contributing to a more effective hydrocarbon exploration impact analysis and facilitating the timely planning by the Alberta government. The results demonstrate notable improvements in both average pixel accuracy (AA) and mean intersection over union (mIoU), ranging from 8.3% to 15.4% and 0.5% to 5.8%, respectively. These enhancements, which have profound implications for the precision of land disturbance detection, prove that the proposed AEC algorithm can serve a dual purpose: correcting errors in the dataset and efficiently detecting land disturbance features in the oil exploration area.

This study aims to map the non-sulphide Zinc (Zn)-bearing gossans at the Gujer Zn deposit area, Central Iran, using Landsat 8 Operational Land Imager (OLI) and GeoEye-1 satellites. The colour composites, Principal Component Analysis (PCA), and Support Vector Machine (SVM) were adopted for image analysis. Zn-bearing gossans contain Fe-oxyhydroxide minerals displaying spectral characteristics in visible and infrared (IR) wavelengths. The application of colour composites using GeoEye-1 images resulted in the delineation of gossans (real target) and ferruginous sandstones (false targets) having the same colour tone in the study area. IR spectroscopy of ore samples showed that hemimorphite exhibits low absorption in shortwave infrared (SWIR) wavelengths. Consequently, the Crosta-PC analysis was conducted using bands 4, 5, SWIR-1, and SWIR-2 of Landsat OLI to enhance only ore gossans. Five target zones were specified using the Crosta technique. The SVM method was performed to increase the accuracy of image analysis using the Radial Basis Function (RBF) kernel. The SVM-RBF method accomplished enhancing ore gossans by defining a new target zone. According to the results, the application of the Crosta technique using bands 4, 5, SWIR-1, and SWIR-2 of Landsat OLI can specify ore gossans and eliminate the interfering effect of ferruginous sandstones in similar geological settings. The SVM-RBF can improve the results of image processing using PC entry of Landsat OLI bands. GeoEye-1 images are useful for the initial assessment of geological units in the region and for delineating the accurate boundary of ore gossans derived from Landsat 8 OLI data.

The technological advancements in spectroscopy give rise to acquiring data about different materials on earth's surface which can be utilized in a variety of potential applications. But, the hundreds of spectral bands are generally equipped with highly correlated information with limited training samples. This will degrade the Hyperspectral Image (HSI) classification accuracy. So Dimensionality Reduction (DR) has become inevitable and necessary step need to incorporate before HSI classification. The main contribution of this work lies in comparative study and review on dimensionality reduction techniques for Hyperspectral remote sensing image classification. The related challenges and research directions are also discussed. This study will help the researchers in the Hyperspectral remote sensing community to choose the appropriate DR technique for classification which can be useful in various real time applications.

Ocean-Atmosphere interactions have been gradually recognized to play a significant role in hydrological cycle and climate change. It is essential to understand ocean-circulation behaviour, including the Sea Surface Salinity (SSS) which is a root cause of variations in sea water density in both coastal system and open ocean. The study has evaluated the performance of SSS obtained from the Soil Moisture and Ocean Salinity (SMOS) satellite data. Daily Barcelona Expert Center (BEC), SMOS, SSS data from 2012 to 2016 are compared with the salinity observations from Argo floats within the Exclusive Economic Zone (EEZ) of Pakistan. Statistics between a daily reporting Argo float and daily SMOS SSS resulted in a spatial correlation, a bias, a standard deviation, and a variance has been examined to determine the monthly, annual and seasonal variations of SSS. Bias analysis showed the underestimation between −0.52 and −0.008 psu while variance has been observed to be between 0.02 and 0.19 psu. The monthly, seasonal and yearly comparison suggests both SMOS and Argo are are found to be in concurrence. Finally, it has been revealed that SSS retrieval algorithm by BEC SMOS provides good estimation along the EEZ of Pakistan.

Landslides, even shallow ones, can displace and destroy the fragile archaeological record. Therefore, it is essential to develop a comprehensive risk assessment and predict the sites at risk before a disaster, which this study aims to provide for 508 archaeological sites associated with Indus civilization and regional Chalcolithic cultures in Gujarat, India. As a hazard inventory for the study area is not available, this study integrates multi-criteria decision-making (MCDM), satellite remote sensing, and Geographic Information Systems (GIS) first to generate a landslide susceptibility map and then to use it for assessing the landslide risk of archaeological sites. Fifteen parameters, viz., elevation, slope, aspect, curvature, average rainfall, drainage density, Topographic Wetness Index (TWI), Stream Power Index (SPI), lithology, soil type, geomorphology, distance from lineaments, Normalized Difference Vegetation Index (NDVI), Land Use Land Cover (LULC), and distance from roads were selected to determine susceptibility. The weights of each parameter were derived using the Analytical Hierarchy Process (AHP). The novelty of this study lies in the spatial overlay of the area of the sites and landslide susceptibility to measure the value loss of the archaeological sites. The results revealed that three of the 508 sites studied are at high risk, and 214 are at medium risk of landslides. With this proposed methodology, this study generates a new dataset on landslide susceptibility for the study area. In addition, it attempts to provide an integrated risk assessment framework for the archaeological sites in India that aids in identifying and mitigating risks.